Keith Vodden Dr. Douglas Smith - Transports Canada

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III ONTARIO MODEL This chapter describes the Ontario model. A. RAW DATA Raw data for the Ontario model come from ORSAR. Exhibit III-1 presents the data for 2004. Exhibit III-1 Raw Data for Ontario Model—Number of Collisions, Fatalities, Injuries and Damaged Vehicles (2004) Collision Severity Fatal Injury PDO TOTAL Collisions 718 49,948 180,882 231,548 Fatalities 799 799 Injuries: Major 245 3,320 3,565 Minor 330 29,589 29,919 Minimal 189 39,338 39,527 Vehicles damaged: Demolished 678 10,023 5,883 16,584 Severe 188 21,021 32,110 53,319 Moderate 147 24,698 99,313 124,158 Light 125 24,486 137,512 162,123 None 45 8,572 19,161 27,778 Unknown 58 5,420 37,511 42,989 In future applications, users will enter ORSAR data for other years to replace the 2004 data. Adjustment factors (developed based on the 2004 data) are applied to other year’s data automatically. These adjustments are discussed in the next section. Note that these ORSAR derived data do not include collisions that are not reported to police. A motor vehicle collision does not need to be reported if: • No personal injury or fatality occurs. • Damage to vehicles is less than $1,000. • No other public or private property is damaged. TNS Canadian Facts, Social and Policy Research 13
Analysis and Estimation of the Social Cost of Motor Vehicle Collisions in Ontario • No criminal activity (drinking and driving, stolen vehicle, driving under suspension) is suspected. The first condition restricts any under-reporting to PDO collisions. As a result, data in ORSAR on the number of PDO collisions and on vehicles involved in PDO collisions are expected to be under-reported in the ORSAR data. We do not have data that would allow us to estimate the number of PDOs not reported to police and subsequently not included in the ORSAR data. Such unreported cases would have a minimal impact on the social cost estimates as they do not, by definition, involve human consequences (fatalities or injuries), or use of resources such as hospital/health care, ambulance, police, courts, and most likely fire departments. (In a later section we use data from the Insurance Bureau of <strong>Canada</strong> that may indicate the number of vehicles and cost of vehicle damage in unreported collisions.) B. ADJUSTMENTS TO THE RAW DATA A number of adjustments are needed to reflect cases of under-reporting and misreporting in the 2004 data. This section presents information on how the model moves from raw data found in ORSAR to adjusted data based on other information. These adjustment factors would be applied automatically if raw data for other years were entered in place of the 2004 ORSAR data. (If updated information used to produce the adjustment factors becomes available the analyst could also replace the information and thereby generate update adjustment factors.) Diagrams are used to illustrate the adjustments built into the model. 1. Unknown damage to vehicles in collision Police reports identify the extent of damage to vehicles involved in collisions. However, for 10% of vehicles involved in the collisions reported in 2004, damage is listed as unknown. By collision severity the percentage of unknown among total vehicles damaged is 5% for fatal, 6% for injury and 11% for PDOs. We have no information that would indicate whether vehicles with unknown damage should be assigned in higher or lower proportion to more sever damage severity categories. For example, vehicles involved in a “hit-and-run” collision and listed as unknown damage may be more likely to have light damage (as the vehicle can be driven away) or severe damage (as the vehicle was involved in a significant collision sufficient to cause the driver to flee the collision scene). As a result, vehicles with unknown damage are assigned based on the known distribution of vehicles by damage severity. The hypothetical example illustrated below shows the allocation of 10 vehicles with unknown damage to other damage categories based on the proportion of vehicles across other damage categories. The number of vehicles is unchanged by the 14 TNS Canadian Facts, Social and Policy Research
Page 1 and 2: TP 14800F Analysis and Estimation o
Page 3 and 4: APPENDIX A: DETAIL ON CALCULATIONS
Page 5 and 6: • Large truck. Collisions involvi
Page 8: ABBREVIATIONS CAC Criteria Air Cont
Page 11 and 12: Analysis and Estimation of the Soci
Page 24 and 25: Ontario Model redistribution. Simil
Page 26 and 27: Ontario Model • Pedestrian Injure
Page 28 and 29: Ontario Model Based on this analysi
Page 30 and 31: Ontario Model Step 3 Allocate addit
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Page 36 and 37: Ontario Model 2.99; specialist—1.
Page 38 and 39: Ontario Model 5. Tow trucks Tow tru
Page 40 and 41: Ontario Model Exhibit III-5 Core WT
Page 42 and 43: Ontario Model We combine this value
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Page 54: Ontario Model We have no data to gu
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Page 66 and 67: Ontario Sub-Models and Their Social
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Ontario Sub-Models and Their Social
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Analysis and Estimation of the Soci
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Appendix A Detail on Calculations o
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APPENDIX B: MAKING CHANGES TO THE M
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Appendix B—Making Changes to the
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Appendix B—Making Changes to the
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Appendix C The Value of Human Conse
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APPENDIX D: VALUE OF HUMAN CONSEQUE
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Appendix D—Value of Human Consequ
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APPENDIX E: REFERENCES Audrey, J. a
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